Density-dependent predation by brown bears (<i>Ursus arctos</i>) on sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
The number of sockeye salmon (Oncorhynchus nerka) killed by brown bears (Ursus arctos) in 13 streams over more than a decade in southwestern Alaska was best explained by an asymptotic increase to about 3000 salmon killed per stream per year as salmon density increased to 10 000 fish·ha1of stream. Divergence from this pattern at some streams probably reflected variation in the number of bears using the stream (which we did not determine) and variation in salmon biomass consumed per fish killed. Daily surveys at one creek over 11 years revealed about 100130 salmon killed per day, ranging from a few to over 600. Higher proportions of the available salmon were killed early and late in the season, when densities were low. Thus the number of salmon killed within and among years increased with salmon abundance but at a declining rate, and the proportion killed generally decreased. Our previous work indicated that the average proportion of salmon killed among streams was controlled mostly by stream size, affecting the ability of bears to catch salmon. These findings are important for understanding the effects of bears on salmon population dynamics and their role in the transport of nutrients from salmon carcasses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".